Literature Index

In "resampling" we broadly include (i) Sampling from populations (Monte Carlo calculations of probability) and (ii) resampling from samples (simple bootstrapping for inference). Both forms of resampling were primarily developed for researchers to answer questions too complex for analytical tools. For many of the modest problems we teach undergraduates, however, analytical solutions are usually available that provide great generality and insight. What need is there for resampling?

Whether the sexes of children born in the same family are independent or whether they are positively or negatively correlated, is an important biological problem, that is interesting also for statisticians.

Many widely-adopted college textbooks that are designed for a student's first (and possibly last) statistics course have incorporated new trends in statistical education, but are organized in a manner that is still driven by a traditional computational, rather than a conceptual, framework. An alternative approach allows for the treatment of many seemingly-unrelated conventional procedures such as one- and two-sample t-tests and analyses of variance and covariance under a unifying prediction model approach. Furthermore, this approach, combined with the power of modern statistical software packages, prepares the student to solve problems beyond the scope of traditional procedures. Students will appreciate the acquisition of practical research capabilities and might even be stimulated to continue their study of statistics.

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an intermediate applied statistics course.